Curriculum & Courses

This course is a foundation course for learning software programming using the Java language. The course will introduce the student to programming concepts, programming techniques, and other software development fundamentals. Students will learn the concepts of Object Oriented programming using Java. The course will present an extensive coverage of the Java programming language including how to write, compile and run Java applications.

The purpose of this course is to learn programming concept and Object Oriented fundamentals using Java. Students will receive a solid understanding of the Java language syntax and semantics including Java program structure, data types, program control flow, defining classes and instantiating objects, information hiding and encapsulations, inheritance, exception handling, input/output data streams, memory management, Applets and Swing window components.

In this course, students will learn concepts that are critical to corporate finance, including: financial statement analysis; performance metrics; valuation of stocks and bonds; project and firm valuation; cost of capital; capital investment strategies and sources of capital, and firm growth strategies. Students will work as individuals and in groups to apply the tools of corporate finance to assigned cases. By the end of this course students will understand:

• How to apply fundamental corporate finance tools to analysis of firms’ strategic financial decisions.

• Evaluate the value impact of corporate decisions.

• Explain the rationale for decisions related to mergers & acquisitions or other corporate transactions and allocations of capital.

• Apply the Four Cornerstones of Corporate Finance in your evaluation of whether a firm has effectively created value.

In this course, students will learn about financial derivative securities: their role in financial management is becoming increasingly important, especially in portfolio management. Students will work on assigned readings, class discussions and examinations .By the end of this course students will be able to:

• Identify valuation of various options and futures as well as their use in risk management. 

• Understand option and futures pricing models, option strategies and index arbitraging. 

• Evaluate common hedging problems and build synthetic derivative positions

In this course, students will learn about the valuation of publicly traded equity securities through case study analyses, class discussion, independent exercises, reading assessments, group work, and  weekly deliverables, culminating in a final investor pitch. 

By the end of the semester students will be able to:

• Perform fundamental analysis ("bottoms-up," firm-level, business and financial analysis)

• Prepare pro forma financial statements, estimate free cash flows and apply valuation models.

• Understand the importance of reasoned analysis and critical thinking when evaluating firms.

Methods of integration, applications of the integral, Taylor's theorem, infinite series.

Vectors in dimensions 2 and 3, complex numbers and the complex exponential function with applications to differential equations, Cramer's rule, vector-valued functions of one variable, scalar-valued functions of several variables, partial derivatives, gradients, surfaces, optimization, the method of Lagrange multipliers. 

Multiple integrals, Taylor's formula in several variables, line and surface integrals, calculus of vector fields, Fourier series.

Matrices, vector spaces, linear transformations, eigenvalues and eigenvectors, canonical forms, applications. 

Special differential equations of order one. Linear differential equations with constant and variable coefficients. Systems of such equations. Transform and series solution techniques. Emphasis on applications.

A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance.

Designed for students in fields that emphasize quantitative methods. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, analysis of variance, confidence intervals and hypothesis testing. Quantitative reasoning and data analysis. Practical experience with statistical software. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.

Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value, confidence intervals, maximum likelihood estimation. 

Introduction to Probability and Statistics

Probability is the foundation on which statistics is built. The purposes of this course are 1) to introduce you to probability and 2) prepare you to take a sequel course on statistical inference (Statistics 4204,5204).

We shall begin by covering the basic axioms of probability and using these in some simple settings. Then we will take up the idea of independence and conditional probability. Following we shall consider random variables, and the properties first of univariate discrete and continuous distributions. When we look at two or more variables, additional considerations arise, such as the relationship between the variables|conditional distributions and marginal distributions. Following these basics, we will then take up some ways of summarizing distributions, e.g., expectations and variances and, in summarizing relationships among variables, covariance and correlation. We then take up some of the more important distributions in statistics. In particular, for the discrete case, we will study the Bernoulli and binomial distributions and the generalization to the multinomial distribution, also the Poisson distribution. For continuous distributions, we take up the univariate and bivariate normal, the Gamma and the Beta distribution. In statistical applications, sums of independent random variables (for example, a sample average is such a sum, divided by sample size) are extremely important and characterizing the properties of these in large samples justifies many of the ways in which we make inferences in statistics. Thus, we take up the properties of these sums in large samples, focusing on stating laws of large numbers and also a simple central limit theorem.

The aim of the course is to describe the two aspects of statistics {estimation and inference {in some details. The topics will include maximum likelihood estimation, Bayesian inference, confidence intervals, bootstrap methods, some nonparametric tests, statistical hypothesis testing, linear regression models, ANOVA, etc.

Linear Regression Models

Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course

Prerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will be covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications